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Fig. 1. Overview of MobileNetV2 Architecture. Blue blocks represent composite convolutional building blocks [19] require the model to make predictions on a relatively large number of classes, in turn, requiring that the model efficiently learn to extract features from photographs in order to perform well on the garbage identification.

Figure 1 Overview of MobileNetV2 Architecture. Blue blocks represent composite convolutional building blocks [19] require the model to make predictions on a relatively large number of classes, in turn, requiring that the model efficiently learn to extract features from photographs in order to perform well on the garbage identification.

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The graph below shows the first-choice-accuracies of these configurations (y-axis), vs the number of calculations required (x-axis), and the size of the model (circle area) Fig. 2. Accuracy between MobileNet and other three pretrained models. [16]
The graph below shows the first-choice-accuracies of these configurations (y-axis), vs the number of calculations required (x-axis), and the size of the model (circle area) Fig. 2. Accuracy between MobileNet and other three pretrained models. [16]
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Software EngineeringImage ProcessingInteractive Multimedia Applications
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